bayesian monte carlo analysis applied to a regional scale transport chemistry model deguillaume l.,...
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Bayesian Monte Carlo analysis applied to a Bayesian Monte Carlo analysis applied to a
regional scale transport chemistry modelregional scale transport chemistry model
Deguillaume L., Beekmann M., Menut L., Derognat C.
Improving emission uncertainties
Characterizing ozone production and
chemical regimes
Over the Ile-de-France region
13/10/2006 - Gloream
Context Photochemical air pollution
EMISSIONS
TRANSPORT
CHEMISTRY
O3OH HO2
NO NO2
NO2 NO
RO2 RO
Reduced VOC Oxidized VOC
ROOH
O3+h
HNO3
H2O2
RCHO +h
hh
+NO2
+ HO2
+ HO2
NOx limited
VOC limited
Context Modelling those processes
Development of a chemistry transport model CHIMERE
(IPSL/ INERIS/LISA)
Model domain : 150×150km
Horizontal resolution : 6×6km grid
Vertical resolution : 8 layers in hybrid pressure coordinates
Chemical mechanism : reduced Melchior
Meteorology: ECMWF
Anthropogenic emissions: EMEP, ARIA, AIRPARIF
Biogenic emissions: Simpson et al. (1999)
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4445464748495051525354
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49.0
1.5 2.0 2.5 3.0
Ile-de-France region
Airparif
Objectives
Observation : Simulated concentrations are very sensitive to emissions
Inverse modelling of emissions from observations (ground based and satellite)
(1) Uncertainties in emissions is always rather large and difficult to estimate
Uncertainties on activity factor, emission factor spatial distribution, temporal
variability...
(2) Evaluation difficult since emitted pollutants undergo chemical transformation
and are tranported away from sources
Problems
Objectives Improving emission uncertainties with a Bayesian approach
Application to semi-climatologic (summers 1998+1999) period for generalization
Adjoint model Kalman filter Bayesian Monte Carlo analysis
Methods : inverse modelling
To verify and improve available
estimates of atmospheric pollutants
emissions
Alternative to bottom-up
construction of emission cadastres
To improve performance of
atmospheric models, especially in
diagnostic studies
To develop a general
observation-based methodology for
estimating parameters of the
atmosphere that cannot be
observed directly
Real world Mathematical world
Concentrations(observations)
Concentrations(simulations)
TransportChemistryDeposition
Transport /Chemistry
model
Emissions Emission cadastre
Comparison
Improve, update
Principle of the Bayesian Monte Carlo analysis
A priori uncertainties
in emissions
A priori uncertainties of
input parameters
« Model uncertainties »
Monte Carlosimulations
A priori concentrations
without constraints
Weighting by observations
A posteriori
distributions of
emissions
Uncertainties
in observations
Correction on a priori distributions of emissions
(also on other perturbed input parameters)
(-) Single correction factor over the whole grid domain and time period of the simulations
(+) Information on the value but also uncertainty associated to the emissions
Mathematical formulation
2
j,
j,kj
j,e
N,1j
YO5.0exp
1
2
1
P(O|Yk)
For each kth Monte Carlo
simulation, the agreement
function:
« Probability to observe a vector of observations O given that the model output Yk is
the true value for the kth Monte Carlo simulation »
Hypothesis: Observations present a normally distributed errors ε
N independent observations Oj
Each simulation is weighted by P(O|Yk) Cost function
A posteriori probability density function vs. a priori one
Results ?
Cumulative probability density function (CPDFs)
(probability that a given model prediction Xk stays below the limit X)
-2 -1.5 -1 -0.5 0 0.5 1 1.5 2
Logarithmic variation
0
0.5
1
1.5
2
No
rm.
pro
ba
bili
ty
ENOx_Working days
Perturbed model input parameters A priori uncertainties of
input parameters
« Model uncertainties »Parameters 1 σ Uncertainty
Emissions
Anthropogenic VOCs + 40
Anthropogenic NOx + 40
Biogenic VOCs + 50
Rate constants
NO + O3 + 10
NO2 + OH + 10
NO + HO2 + 10
NO + RO2 + 30
HO2 + HO2 + 10
RO2 + HO2 + 30
RH + OH + 10
CH3COO2 + NO + 20
CH3COO2 + NO2 + 20
PAN + M + 30
Photolysis frequencies and radiation
Actinic fluxes + 10
J(O3 2 OH) + 30
J(NO2 NO + O3) + 20
J(CH2O CO + 2 HO2) + 40
J(CH3COCO …) + 50
Meteorological parameters
Zonal wind speed + 1
Meridional wind speed + 1
Mixing layer height + 40
Temperature + 1.5
Relative humidity + 20
Vertical mixing coefficient + 50
Others
Deposition velocity + 25
O3OH HO2
NO NO2
NO2 NO
RO2 RO
Reduced VOC Oxidized VOC
ROOH
O3+h
HNO3
H2O2
RCHO +h
hh
+NO2
+ HO2
+ HO2
Log-normal distribution
Uncertainty ranges uncertainty assessment studies and expert judgements
Measurement constraintsobservations
1.6 1.7 1.8 2.0 2.22.11.9 2.72.3 2.6 2.82.52.4 2.9 3.048
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LATITUDE
LONGITUDE
PARIS
Nd
1k
Ns
1j
Nh
1i3 )k,j,i(obs
NhNsNd
1)O,NO(OBS
(1) Urban NO and O3
NO direct forcing for NOx emissions
O3 information on ozone precursor
emissions (VOC, NOx)
AIRPARIF NETWORK
(2) Rural O3 buildup
The two daily maximal [O3] O3 plume
The 3 lowest [O3] O3 background
For simulations and observations:
+(1) Days where the maximal [O3] is observed and
simulated at the same or neighbouring station
(2) [O3 max] - [O3 back] > 10 ppb for simulations
and observations
Results – Cumulative probability
Blue without constraints
Simulated urban NO, O3 and O3 production in the plume- summers 1998 + 1999
Uncertainties are reduced by a factor :3.2
2.4
1.7
Deguillaume et al., in press, JGR, 2006
60 80Urban NO (ppb)
0
0.2
0.4
0.6
0.8
1
Cum
ula
tive p
rob
abilit
y
40200
Urban NO climato 98+99
7020 30 40 50 60
Urban O3 (ppb)
0
0.2
0.4
0.6
0.8
1
Cum
ula
tive
pro
bab
ility
Urban O3 climato 98+99
40
O3 in plume (ppb)
0
0.2
0.4
0.6
0.8
1
Cum
ula
tive p
rob
abilit
y
0 10 20 30
O3 plume climato 98+99
2σ 2σ
2σ 2σ 2σ
2σ
Totalconstraints
Red Total constraints
Results – Probability density functions
Blue histogram a priori distribution with 40% 1σ uncertainty
Emissions of anthropogenic NOx and VOC – cumulative summers 1998 and 1999
1σ uncertainty : 22 % for NOx , 31% for VOC emissions
NOx emissions remain nearly unchanged - VOC emissions are enhanced (+16%)
Better fit the observations in 1999 vs. 1998 (in 1998, the constraints do not act in a similar way)
-1.2 -0.8 -0.4 0 0.4 0.8 1.2Log. variation
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0.4
0.8
1.2
1.6
2
Norm
. p
rob
ab
ilit
y
ENOx_98+99_climato
-1.2 -0.8 -0.4 0 0.4 0.8 1.2
Log. variation
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1.6
2
Norm
. p
rob
ab
ilit
y
EVOC_98+99_climato
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0.4
0.8
1.2
1.6
Log. variation
EVOC/ENOx_98+99_climato
Totalconstraints
Red lines a posteriori distribution
Conclusion & perspectives
Bayesian Monte Carlo uncertainty analysis Semi-climatologic period
Ile-de-France region
A posteriori PDF NOx emission unchanged average
reduced standard deviation (20% vs. 40%)
VOC emission enhancement (+16%)
reduced standard deviation (30% vs. 40%)
Uncertainty in the simulated urban NO, urban O3 and O3 production in the plume are
strongly reduced
indirect constraints on VOC emissions (urban O3 and O3 production) lower reduction
Adjustements in the other model input parameters (vertical mixing coefficient, 1998)
Better fit in 1999 than in 1998 because constraints in 1998 do not act in a similar way
Other region ? Marseille area
(ESCOMPTE, AIRMAIX network)Satellite measurements
Objectives Characterizing ozone production and chemical regimes
Better understand the buildup of pollution episodes around Paris region
Perspectives…
Application to semi-climatologic (summers 1998+1999) period for generalization
Direct simulation Bayesian Monte Carlo approach
Constraints by observations
Emission reduced scenario (-30% NOx and -30% VOC emissions)
Analysis of the chemical regime over the Ile de France region
2 approachesMethodology
(2) Characterizing ozone production and chemical regimes
Preliminary results ...
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Anticyclone sur l’ocean mouvement aiguille montre vent vers le sud
NOx -30% VOC -30%Reference
Daily maximum of O3 averaged over summers 1998 and 1999
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